Optical Transmitter, Method and Storage Medium in Optical Network

ABSTRACT

Embodiments are disclosed of methods performed at an optical transmitter, devices and computer-readable storage media. For example, the method includes performing a process to select a PAM value from a set of candidate PAM values based on currently transmitted training data bits, transmitted Pulse Amplitude Modulation (PAM) values corresponding to previously transmitted training data bits, and training data bits to be transmitted subsequently, the selected PAM value corresponding to the currently transmitted training data bits. The selected PAM value is transmitted to an optical receiver, and an indication as to whether the optical receiver correctly detects the currently transmitted training data bits is received from the optical receiver. Then, the process is updated at least in part based on the indication. The manner of performing neural network learning at an optical transmitter side to obtain an encoding strategy can maximize the channel allowable capacity.

FIELD

Embodiments of the present disclosure generally relate to an opticalnetwork, and more specifically, to an optical transmitter, method andcomputer readable storage medium in an optical network.

BACKGROUND

Due to the use of optical fibers, FTTx (Fiber To The X, fiber access) iswidely used as a new generation of bandwidth solution to provide userswith a high-bandwidth, full-service access platform. FTTH (Fiber To theHome, which connects the optical fibers directly to users' home) is evenreferred to as the most ideal business transparent network, and is theultimate way of access network development. In the optical fiber accessnetwork, after an optical transmitter encodes, modulates, andelectro-optically converts a signal, the optical transmitter transmitsthe signal via an optical link to an optical receiver, and the opticalreceiver decodes, demodulates, and electro-optically converts thereceived signal to recover the originally transmitted signal.

Because the optical link has a non-Gaussian, nonlinear channel response,it generates inter-symbol interference on the signal, causing the signalrecovered at the optical receiver side to be different from theoriginally transmitted signal. In this case, transmission errors occur,and the channel capacity utilization is reduced greatly. In order todeal with various impairments caused in the transmission link,intelligent and efficient equalization algorithms, such aspost-equalization Maximum Likelihood Sequence Estimate (MLSE)/Viterbifor dual binary recovery, or the like, are usually used on the opticalreceiver side to backwards compensate for impairments to the signal.These algorithms are usually used on the optical receiver side andrarely used on the optical transmitter side. Existing preprocessing inan optical transmitter is either fixed or experience-based, with verylimited flexibility. Due to the lack of an effective preprocessingmethod on signals at the optical transmitter side, the allowablecapacity of the link cannot be fully utilized.

SUMMARY

Generally, embodiments of the present disclosure propose methodsperformed at an optical transmitter, devices, and computer readablestorage media.

In a first aspect, embodiments of the present disclosure provide amethod performed at an optical transmitter. In the method, a process isperformed to select a PAM value from a set of candidate PAM values basedon currently transmitted training data bits, transmitted Pulse AmplitudeModulation (PAM) values corresponding to previously transmitted trainingdata bits, and training data bits to be transmitted subsequently, theselected PAM value corresponding to the currently transmitted trainingdata bits. The selected PAM value is transmitted to an optical receiverand an indication as to whether the optical receiver correctly detectscurrently transmitted training data bits is received from the opticalreceiver. Then, the process is updated at least in part based on theindication.

In a second aspect, the embodiments of the present disclosure provide anoptical transmitter. The optical transmitter comprises at least oneprocessor and at least one memory storing computer program code. The atleast one memory and computer program code are configured, together withthe at least one processor, to cause the optical transmitter to: performa process to select a PAM value from a set of candidate PAM values basedon currently transmitted training data bits, transmitted Pulse AmplitudeModulation (PAM) values corresponding to previously transmitted trainingdata bits, and training data bits to be transmitted subsequently, theselected PAM value corresponding to the currently transmitted trainingdata bits; transmit the selected PAM value to an optical receiver;receive, from the optical receiver, an indication as to whether theoptical receiver correctly detects the currently transmitted trainingdata bits; and update the process at least in part based on theindication.

In a third aspect, the embodiments of the present disclosure provide acomputer readable storage medium having computer program instructionsstored thereon. The instructions, when executed by a processor on anoptical transmitter, cause an optical transmitter to perform the methodin the first aspect.

It would be appreciated that the content described in the Summarysection is not intended to limit key or significant features ofembodiments of the present disclosure, nor is it intended to be used tolimit the scope of the present disclosure. Other features of the presentdisclosure will become readily understood from the followingdescription.

BRIEF DESCRIPTION OF THE DRAWINGS

Several example embodiments will be described below with reference tothe accompanying drawings, in which:

FIG. 1 illustrates an example architecture of functional modules in theexisting optical network;

FIG. 2 illustrates an example architecture of an optical networkaccording to the present disclosure;

FIG. 3 illustrates an example training process for an opticaltransmitter according to some embodiments of the present disclosure;

FIG. 4 illustrates selecting a PAM value corresponding to the currentlytransmitted training data bits according to embodiments of the presentdisclosure;

FIG. 5 illustrates a block diagram according to a specific embodiment ofthe present disclosure;

FIG. 6 illustrates a flowchart according to a specific embodiment of thepresent disclosure;

FIG. 7 illustrates an example diagram of a comparison of an originallytransmitted sequence and PAM8 values output by a non-linear opticaltransmitter according to some specific embodiments of the presentdisclosure;

FIG. 8 illustrates an example diagram of a Probability DistributionFunction (PDF) output of PAM8 after a Deep Q Learning Network (DQN)according to some specific embodiments of the present disclosure;

FIG. 9 illustrates a schematic diagram of bit error rate decreasing withthe iteration of DQN according to some specific embodiments of thepresent disclosure; and

FIG. 10 illustrates a flowchart of a method according to someembodiments of the present disclosure; and

FIG. 11 illustrates a block diagram of a device adapted to implementembodiments of the present disclosure.

Throughout the drawings, the same or similar reference symbols refer tothe same or similar elements.

DETAILED DESCRIPTION OF EMBODIMENTS

Embodiments of the present disclosure will now be described in detailwith reference to the accompanying drawings. Although the drawingsillustrate some embodiments of the present disclosure, it would beappreciated that the present disclosure may be implemented in variousforms and should not be construed as limited to the embodiments setforth herein. Rather, these embodiments are provided to understand thepresent disclosure more thoroughly and completely. It should beunderstood that the drawings and embodiments of the present disclosureare for exemplary purposes only, and are not intended to limit theprotection scope of the present disclosure.

The term “training process” or “learning process” as used herein refersto a process that utilizes experience or data to optimize systemperformance. For example, an optical transmitter can optimize anencoding strategy step by step through a training process or a learningprocess, such as encoding a transmitted sequence into a morenoise-resistant encoding sequence. In the context of the presentdisclosure, the term “training” or “learning” can be usedinterchangeably for convenience of discussion.

The term “optical transmitter” as used herein refers to any appropriatedevice capable of transmitting information in an optical network.Examples of an optical transmitter include, but are not limited to, oneor more of the following: an Optical Line Terminal (OLT), an OpticalNetwork Unit (ONU), an Optical Network Terminal (ONT), an OpticalDistribution Network (ODN), and the like.

The term “optical receiver” as used herein refers to any appropriatedevice capable of receiving information in an optical network. Examplesof an optical receiver include, but are not limited to, one or more ofthe following: an Optical Line Terminal (OLT), an Optical Network Unit(ONU), an Optical Network Terminal (ONT), an Optical DistributionNetwork (ODN), and the like.

The term “circuit” as used herein refers to one or more of thefollowing:

(a) a hardware circuit-only implementation (such as an analog and/ordigital circuit-only implementation); and

(b) a combination of hardware circuits and software, such as (ifapplicable): (i) a combination of analog and/or digital hardwarecircuits and software/firmware, and (ii) a combination of any portion ofa hardware processor and software (including digital processors,software, and memories that work together to enable a device, such asOLT or other computing devices, to perform various functions); and

(c) hardware circuits and/or a processor, such as a microprocessor or apart of a microprocessor, that requires that software (e.g. firmware)for operation, but may be absent when software is not required foroperation.

The definition of circuit applies to all usage scenarios of this term inthe present application (including any claim). For another example, theterm “circuit” as used herein also covers implementations of onlyhardware or a processor (or processors), or a part of a hardware circuitor a processor, or its accompanying software or firmware. For example,if applicable to a particular claim element, the term “circuit” alsocovers a baseband integrated circuit, or a processor-integrated circuit,or a similar integrated circuit in the OLT or other computing devices.

The term “including” and its variants used herein are to be read asopen-ended, that is, “including but not limited to.” The term “based on”is to be read as “based at least in part on.” The term “one embodiment”is to be read as “at least one embodiment;” and the term “anotherembodiment” is to be read as “at least one other embodiment.” Relateddefinitions of other terms will be given in the description below.

As described above, in a fiber access network, an optical transmittertransmits a signal to an optical receiver via an optical link. Becausethe optical link is a non-linear, bandwidth-limited, dispersive andnoisy channel, it will cause interference to the transmission of thesignal.

In order to detect and resolve such transmission errors, it is common touse an effective equalization and compensation algorithm on the opticalreceiver side. In previous work such as 25G-PON or 50G-PON, adaptivealgorithms are implemented on the optical receiver side, for example,Least Mean Square (LMS) algorithm-based or Artificial IntelligenceNeural Network (AI-NN)-based equalization and MLSE decoding, but none ofthe adaptive algorithms are performed on the optical transmitter side.In contrast, for the interference introduced by the link, the opticaltransmitter side does not have a clear guiding strategy when setting upits parameters (e.g., modulation, encoding, bias, drive circuit,modulation depth, and the like). In other words, all preprocessingimplemented on the optical transmitter side is based on experience, forexample, pre-emphasis or dual binary encoders.

Therefore, it is important to require an optical transmitter to performa training process that automatically adjusts its configuration step bystep to maximize channel capacity utilization.

FIG. 1 illustrates an example architecture of functional modules in anexisting optical network 100. The network 100 is generally composed ofsoftware and hardware functional blocks, including: an encoding andmodulating unit 120, an optical transmitter 130, a channel 140, anoptical receiver 150, and CDR/MISE/NN 160 for identifying 0/1. Becausethe channel 140 causes interference to the transmission of the signal,the received sequence recovered at the optical receiver 150 side will bedifferent from the original sequence transmitted at the opticaltransmitter 130. For example, in FIG. 1, a sequence 110 transmitted onthe optical transmitter side is “001011”, and the received sequence 170decoded and recovered on the optical receiver side is “011001.” It canbe seen that the transmission errors of the “0” of the second bit andthe “1” of the fifth bit in the transmitted sequence 110 occur, and arerecovered to the bits 180 “1” and “0” in the received sequence 170 onthe optical receiver side, respectively.

An effective equalization and compensation algorithm may be used on theoptical receiver 150 side, which performs a training process thatcompares the accurately received content with respective original data(pilot or training sequence known in advance), and mapping relationshipis learned accordingly, and then the optical receiver knows how toequalize or compensate any new input distorted data back to its originalform. Equalization and compensation algorithms can employ a CDRalgorithm, MLSE algorithm, Neural Network (NN) algorithm, or the like.

The optical network 100 shown in FIG. 1 needs to eliminate theinter-symbol interference introduced by the imperfection of the channel.In the optical network 100, the equalization and compensation algorithmis used only on the optical receiver 150 side, so that the opticalreceiver learns the equalization or compensation strategy and correctlyrecovers the originally transmitted sequence from the received sequence.In contrast, on the optical transmitter 130 side, only fixed,experience-based preprocessing is used, and the lack of a clearalgorithm requires the optical transmitter 130 to evolve or adjust itssettings in a specific direction step by step for parameteroptimization. This makes it impossible to fully utilize the allowablecapacity of the link even with a perfect optical receiver 150 anddecoding technology.

Inventors noticed that the lack of preprocessing/encoder on the adaptiveoptical transmitter 130 side is mainly due to the fact that theend-to-end response is complex and implicit. Real optical channels arenon-Gaussian, non-ideal bandwidth-limited systems, making theinter-symbol interference complicated. Moreover, there is an implicitsuperposition effect between the configuration in the opticaltransmitter, the channel condition, and the detection method anddecoding algorithm in the optical receiver.

Inventors also noticed that in the optical receiver, there are also manydecoding methods, which may be original CDR, or a post-equalizer with afew taps, or MLSE-based sequence estimation. In contrast, there is noclear algorithm that requires the optical transmitter to perform atraining process to evolve or adjust its settings in a specificdirection step by step for parameter optimization. Therefore, there is aneed to require the optical transmitter to perform a training process tolearn an encoding strategy through a clear indication on the lighttransmitter side. This optical network can adaptively use the encodingstrategy obtained through machine learning on the optical transmitterside to maximize the channel capacity utilization.

Embodiments of the present disclosure propose a solution for a trainingprocess of an optical transmitter in an optical network. According tothe solution, the optical transmitter can perform a training process toautomatically adjust its configuration, so as to maximize the channelcapacity. The optical transmitter performs a process to select a PAMvalue from a set of candidate PAM values based on currently transmittedtraining data bits, transmitted Pulse Amplitude Modulation (PAM) valuescorresponding to previously transmitted training data bits, and trainingdata bits to be transmitted subsequently, a training process or alearning process the selected PAM value corresponding to the currentlytransmitted training data bits; and transmits the selected PAM value toan optical receiver. After receiving, from the optical receiver, anindication as for whether the optical receiver correctly detects thecurrently transmitted training data bits, the optical transmitterupdates the process at least in part based on the indication. Therefore,the optical transmitter can adjust automatically its configuration usingthe indication from the optical receiver to obtain the encoding strategythrough machine learning.

According to embodiments of the present disclosure, the opticaltransmitter may automatically update the training process step by stepbased on the indication of the optical receiver, and using neuralnetwork learning or a table lookup encoding strategies. Through neuralnetwork learning or a table lookup, the output and input of an encodingcomponent of the optical transmitter can be compared to determine thedifference between the two, and the encoding component can update theencoding strategy by feeding the difference back to the encodingcomponent. In this way, the channel capacity utilization can bemaximized and various conditions of all single optical receivers in anoptical network can be adaptively satisfied in an automatic manner.

In some embodiments, the above solution may be implemented in an OTL ina Passive Optical Network (PON). For example, a 50 Gb/s Passive OpticalNetwork (PON) with Non-Return-to-Zero (NRZ) format can be implemented inOLT using only a 20G bandwidth optical transmitter and a 3-bitlow-resolution digital-to-analog converter (DAC). At the same time, nohardware update is required in the ONU, and no special decoding orequalization algorithm is required in the ONU. Only some new messagesare required to be fed back to the OLT to help the reinforcementlearning in the OLT perform intelligent encoding.

In such an optical network, it is implementation by this intelligentautomatic encoding before transmission, which allows the opticaltransmitter to take advantage of the nonlinear channel response andachieve channel capacity. More importantly, traditional low-cost ONUScan be used without manual assistance, as it can all be automated.

FIG. 2 illustrates an example architecture of an optical network 200.The optical network 200 includes an optical transmitter 230, a channel240, and an optical receiver 250. The optical transmitter 230 encodesand modulates a transmitted sequence, making it an encoded sequencesuitable for transmission over channel 240, and transmits the encodedsequence to channel 240. The optical transmitter 230 also receives anindication as to whether the optical receiver 250 correctly detects thetransmitted sequence from the optical receiver 250, and performs neuralnetwork learning or a table lookup to learn and evolve the encodingstrategy of the optical transmitter 230, so that it uses the encodingstrategy to encode the transmitted sequence in the subsequenttransmission.

In the network 200, channel 240 transmits the sequence transmitted bythe optical transmitter 230 to the optical receiver 250. Channel 240 hasa non-Gaussian, non-linear channel response with inter-symbolinterference. Therefore, signals transmitted on channel 240 are subjectto inter-symbol interference and transmission errors occur.

In the example shown in FIG. 2, upon receiving the encoded sequence, theoptical receiver 250 decodes the received encoded sequence to recoverthe received sequence. Due to the non-linearity of channel 240,inter-symbol interference is introduced, so some bits in the recoveredsequence may have errors. The optical receiver may compare the sequencesbit by bit and feed back the optical transmitter 230 with an indicationof the correctness of each bit. The optical receiver 250 may alsoequalize or compensate the recovered error bits back to the originaltransmitted bits according to the mapping relationship learned by usingan effective equalization and compensation algorithm.

FIG. 3 illustrates an example communication and operation process 300 ofan optical transmitter 230 and an optical receiver 250 according to someembodiments of the present disclosure. The optical transmitter 230 mayperform (310) a process (e.g. a learning or training process) to selecta PAM value from a set of candidate PAM values based on currentlytransmitted training data bits, one or more transmitted PAM valuescorresponding to previously transmitted training data bits, and one ormore training data bits to be transmitted subsequently, the selected PAMvalue corresponding to the currently transmitted training data bits,thereby encoding the currently transmitted training data bits into PAMvalues. Considering that the real channel is non-Gaussian,bandwidth-insufficient, and nonlinear, the output of the trainingprocess will not only be determined by the currently transmitted bits,but also the output of the previous steps (the transmitted PAM valuescorresponding to the previously transmitted bits) and several bits to betransmitted subsequently.

In some embodiments, the process may also be initialized, andinitializing the process includes at least one of the following ispredefined: the number of the transmitted PAM values corresponding tothe previously transmitted training data bits, the number of trainingdata bits to be transmitted subsequently, an order of the PAM, and avalue of an indication as to whether the optical receiver correctlydetects the currently transmitted training data bits.

In some embodiments, the process may be performed by neural networklearning or by a table lookup. Examples of neural network learning or atable lookup include, but are not limited to, Deep Q Network (DQN), a“status-action” Two-Dimensional Table (Q-Table), and the like. Anexample process of performing a process to select, from a set ofcandidate PAM values, a PAM value corresponding to the currentlytransmitted training data bit is now described in detail with referenceto FIG. 4.

FIG. 4 illustrates an example process 400 of selecting a PAM valuecorresponding to the currently transmitted training data bit accordingto embodiments of the present disclosure. As an example, an encoder 405may be introduced in the optical transmitter 230.

In process 400, a training sequence includes two bits of training databits 410 to be transmitted subsequently, one bit of the currentlytransmitted training data bits 415, and four bits of PAM values 425corresponding to previously transmitted training data bits. A 3-bit PAM(i.e., PAM8) is used, and the respective set of candidate PAM values is(1, 0.7143, 0.4286, 0.1428, −0.1428, −0.4286, −0.7143, −1). For example,the training sequence includes two training data bits 410 (“0” and “1”)to be transmitted subsequently, one currently transmitted training databit 415 (“0”), and transmitted PAM values 425 (referred to as “previousstatus” below, for ease of description) (“0.1428,” “−0.4285,” “0.7143”and “0.4285”) corresponding to four previously transmitted training databits 420 (“1,” “0,” “1” and “1”). In this embodiment, the number of theprevious statuses 425 and the number of the training data bits 410 to betransmitted subsequently may be flexibly selected according to needs.This training sequence is used as an input of the encoder 405, and theencoder performs, based on the input, a process to select 440, from aset of candidate PAM values, a PAM value corresponding to the currentlytransmitted training data bits 415.

In some embodiments, the number and value of the candidate PAM valuesare related to the order of the selected PAM. In order to simplify theprocess and save costs, in some embodiments, a 3-bit PAM (i.e., PAM8) isselected.

In some embodiments, by performing the process, a probability of acandidate PAM value of the set of candidate PAM values being selectedcan be determined; and based on the determined probability, the PAMvalue corresponding to the currently transmitted training data bits maybe selected. In some embodiments, a set of candidate PAM values may bedetermined based on the order of the selected PAM. Taking DQN and PAM8as an example, a set of candidate PAM values associated with PAM8 is (1,0.7143, 0.4286, 0.1428, −0.1428, −0.4286, −0.7143, DQN outputs eightoptions 435 based on the input, each of which corresponds to a value inthe set of candidate PAM values of the 3-bit PAM8, which indicates aprobability (e.g., a probability indication) of the PAM value isselected as the PAM value corresponding to the currently transmittedtraining data bits. For example, DQN outputs 435 {1, 0.7143, 0.4286,0.1428, −0.1428, −0.4286, −0.7143, −1}, and each value is assigned adifferent probability indication 460 for indicating a probability ofbeing selected as the PAM value corresponding to the currentlytransmitted training data bit. The PAM value with the maximumprobability indication is selected (440) from the 8 probabilityindications output by DQN above. As shown in FIG. 4, −0.1428 is themaximum probability indication, indicating that, for the currentlytransmitted training data bit, the DQN intends to encode it into −0.1428currently.

In some embodiments, transmitting the selected PAM value to the opticalreceiver 250 includes: performing digital-to-analog converting on theselected PAM value; and transmitting the converted selected PAM value tothe optical receiver 250. Specifically, the PAM value −0.1428 may betransmitted to a digital to analog converter (DAC) 445, which isconverted into an analog signal and a drive voltage of −0.1428 is outputto the optical transmitter 230.

Continuing to refer to FIG. 3, the optical transmitter 230 transmits(320) the selected PAM value 445 to the optical transceiver 250. Theoptical receiver 250 detects (330) the currently transmitted trainingdata bits, determines whether the currently transmitted training databits are correctly detected, and generates an indication as to whetherthe optical receiver 250 correctly detects the currently transmittedtraining data bits. In an embodiment, the optical receiver 250 comparesthe received training data bits with the corresponding originallytransmitted training data bits (a training sequence known in advance),so as to determine whether the currently transmitted training data bitsare correctly detected, and generates an indication as to whether theoptical receiver 250 correctly detects the currently transmittedtraining data bits. The indication may be generated in any appropriatemanner. As an example, the correctly detected training data bit may beassigned a different value from the error detected training data bit.For example, a greater value “+0.1” is assigned to the correctlydetected training data bit, while a smaller value “−0.9” is assigned tothe error detected training data bit. Then, the optical receiver 250transmits (340) the indication to the optical transmitter 230.

At block 350, the optical transmitter 230 updates the process at leastin part based on the indication. The updated process is compared with aconvergence condition; in response to the updated process not satisfyingthe convergence condition, 310 to 350 in the process 300 are repeateduntil the convergence condition is satisfied. For example, the opticaltransmitter 230 repeats performing 310 to 350 (360) until the process isconverged.

Still take DQN as an example, there are two networks in DQN that haveexactly the same structure but different initialization parameters. Thenetwork MainNet (referred to as the first network below, for ease ofdescription) that predicts the estimation of Q uses the latestparameters, while the neural network TargetNet (referred to as secondnetwork below, for ease of description) that predicts the reality of Quses the parameters of a long time ago. Q1 (s,a;θi) represents an outputof the current first network, which is used to evaluate a value functionof the current status-action pair; Q2 (s,a;θi) represents an output ofthe second network, which can be solved for targetQ. Therefore, when anaction is taken on the environment, the Q can be calculated based on Q1(s,a;θi) and Q2 (s,a;θr), and the parameters of the first network can beupdated based on a cost function. After a certain number of iterations,the parameters of the first network are replicated to the secondnetwork. As such, a learning process is calculated, so that thecalculated Q is closer to targetQ.

Specifically, in this embodiment, the update speed of the first networkis different from that of the second network. The first network isupdated quickly, and the parameters of the first network are replicatedto the second network every certain number of iterations. In eachiterative calculation of the first network, for the input values, thatis, the currently transmitted training data bits (for example, 415 inFIG. 4), the transmitted PAM values (for example, 425 in FIG. 4)corresponding to the previously transmitted training data bits, and thetraining data bits to be transmitted subsequently (for example, 410 inFIG. 4), calculate the probability (for example, the probabilityindication 460 in FIG. 4) that each PAM value becomes the PAM value intowhich the current DQN desires to encode the currently transmitted bits.The maximum probability indication 460 is selected in the output, andafter the maximum probability indication is multiplied by a delay effectparameter, a temporary result is obtained by adding the multiplicationproduct to the indication received from the optical receiver. For thesame input values, the second network calculates a probability (forexample, the probability indication 460 in FIG. 4) that each PAM valuebecomes the PAM value into which the current DQN desires to encode thecurrently transmitted training data bits, and finds the position wherethe maximum probability indication is located. The temporary resultcalculated by the first network is used as the probability indication ofthe PAM value at the position where the maximum probability indicationfound in the second network is located, and the modified output value inthe second network is assigned to the output value of the first network.Then, neural network learning is performed on the first network untilthe first network satisfies a convergence rule. At this point, the firstnetwork iterative learning process is completed and the above process isrepeated. Until the parameters coverage of the first network to thesecond network is terminated, the termination rule is the same as theconvergence rule. In some specific embodiments, the convergence rule canuse any of the following: 1. a number of parameter coverage of the firstnetwork to the second network reaches a set upper limit; 2, the entropyor damage function value is less than or equal to a threshold; 3. thegradient value is less than or equal to a threshold; and 4. a number ofiterations of the first network reaches a set upper limit.

The optical receiver 250 feeds back to the optical transmitter 230 anindication as to whether the currently transmitted training data bitsare detected correctly, the optical transmitter 230 may perform, basedon the indication, neural network learning or a table lookup, so thatthe encoder in the optical transmitter 230 can automatically learn theencoding strategy. By performing the encoding strategy prior totransmission, the channel capacity utilization can be automaticallymaximized, and at the same time, it can be compatible with any existingdetection and decoding methods on the optical receiver side, without theneed to update the optical receiver hardware and the equalizationalgorithm.

FIG. 5 illustrates a block diagram according to a specific embodiment ofthe present disclosure.

In FIG. 500, in the encoder 405, the following are performed including:reviewing a training data bit 410 “0” to be transmitted subsequently, acurrently transmitted training data bit 415 “0”, and previous statuses425 corresponding to four previously transmitted training data bits 420“1,” “0,” “1” and “1”; determining, based on DQN or Q-Table, a PAM value450 corresponding to the currently transmitted training data bit;transmitting it to the DAC 445; and after converting it into an analogsignal, a drive voltage of the PAM value 450 is output. The PAM value450 is transmitted to the optical receiver via channel 240.

Due to the inter-symbol interference in channel 240, the transmitted PAMvalue 450 (−0.1428) becomes a wrong PAM value 510 (−1.043). Uponreceiving the wrong PAM value, the optical receiver 250 performsdecoding recovery using CDR or MLSE to recover it to a 0/1 bit 520(“1”). Since the optical receiver 250 knows the originally transmittedtraining sequence of the optical transmitter 230, the optical receiver250 may compare the currently detected bit 520 (“1”) with the originallytransmitted (“0”) of the optical transmitter 230. Thus, it is found thatan error occurs in the currently detected bit 520, and an indication 530is generated, indicating that the bit has an error, and it is assigned avalue of −0.9.

The optical receiver 250 feeds back the indication 530 to DQN or Q-tablein the encoder 405 for neural network learning or a table lookup.

The above process is repeated until the convergence condition of theneural network is satisfied.

FIG. 6 illustrates a flowchart according to a specific embodiment of thepresent disclosure. The process 600 includes a training process and animplementation process. For example, the optical transmitter is OLT 605,and the optical receiver is ONU 610.

In the training process, at block 615, OLT initializes parameters of itsDQN, and the parameters include at least the following dimensions: 1. anumber of previous PAM values; 2. a number of future 0/1 bits; 3. orderof the output PAM; and 4. a reserved indication value. At block 620, theoptical transmitter generates a training 0/1 sequence. The sequence isencoded by DQN, and its output is stored in a buffer for future use. Atblock 625, the DQN output (a PAMX signal) is converted in the DAC, anddrives OLT to transmit the output to ONU. At 630, ONU detects andrecovers the 0/1 bit of the training sequence using an equalizationalgorithm such as CDR or MLSE, or the like. At 640, the optical receiver610 compares the 0/1 sequence bit by bit and feeds back to the opticaltransmitter 605 the correctness of each bit, namely the indication ofeach bit. At block 635, the indication of each bit will be filled in thereserved positions in DQN, and DQN thus updates its parameters in aReinforcement Learning (RL) manner. At block 645, it is determinedwhether DQN satisfies the convergence condition. If yes, the trainingprocess is ended; otherwise, blocks 615 to 645 are repeatedly performeduntil the convergence condition is satisfied.

In the implementation process, at block 650, DQN parameters are fixed,and DQN can encode actual data (i.e., payload), and then transmits theencoded data to ONU through the channel. At block 655, ONU may receiveand detect data normally using an equalization algorithm.

By performing reinforcement learning on the optical transmitter 230, theoptical transmitter can learn automatically the encoding strategyadapted to the entire end-to-end optical link. The optical receiver 250does not need any change in hardware, and can use the original decodingalgorithm, which only needs to pass some message, for example, feedingback the error information of each training sequence bit. In this way,the allowable channel capacity can be maximized.

Take an experiment using a passive PON with insufficient bandwidth as anexample. As DQN gradually learns and evolves, it will output PAM8signals very intelligently. FIG. 7 illustrates an example diagram of acomparison of an originally transmitted sequence and a PAM8 value outputby a nonlinear optical transmitter according to some specificembodiments of the present disclosure, where 710 indicates theoriginally transmitted sequence, and 720 indicates the PAM8 value outputby the nonlinear optical transmitter. FIG. 8 illustrates an examplediagram of a Probability Distribution Function (PDF) of a PAM8 after aDeep Q Learning Network (DQN) according to some specific embodiments ofthe present disclosure. It can be seen that after neural networklearning or a table lookup, the optical transmitter outputs a PAM8 valuethat does not conform to the originally transmitted sequence. FIG. 9illustrates a schematic diagram of bit error rate decreasing with theiterations of DQN according to some specific embodiments of the presentdisclosure. Using the encoding strategy obtained through neural networklearning or a table lookup in FIG. 6 may bring significant advantages inbit error rate, because the encoding strategy can encode the original0/1 sequence into a waveform adapted to channel transmission and thusmaximize the allowable channel capacity.

FIG. 10 illustrates a flowchart of an example method 1000 according tosome embodiments of the present disclosure. The method 1000 may beimplemented at the optical transmitter 230 as shown in FIG. 2. For easeof discussion, the method 1000 is described in detail below withreference to FIG. 2.

As shown, at block 1010, the optical transmitter 230 performs a processto select a PAM value from a set of candidate PAM values based oncurrently transmitted training data bits, transmitted Pulse AmplitudeModulation (PAM) values corresponding to previously transmitted trainingdata bits, and training data bits to be transmitted subsequently, theselected PAM value corresponding to the currently transmitted trainingdata bits. At block 1020, the selected PAM value is transmitted to theoptical receiver 250. At block 1030, an indication is received as towhether the optical receiver correctly detects the currently transmittedtraining data bits. At block 1040, the process is updated at least inpart based on the indication.

In some embodiments, the process is performed by neural network learningor by a table lookup.

In some embodiments, at least one of the following is predefined: thenumber of the transmitted PAM values corresponding to the previouslytransmitted training data bits, the number of the training data bits tobe transmitted subsequently, an order of the PAM, or a value of theindication as to whether the optical receiver correctly detects thecurrently transmitted training data bits.

In some embodiments, transmitting the selected PAM value to the opticalreceiver comprises: performing digital-to-analog conversion on theselected PAM value; and transmitting the converted selected PAM value tothe optical receiver.

In some embodiments, performing the process comprises: performing theprocess to determine a probability that a candidate PAM value isselected from the set of candidate PAM values; and selecting, based onthe determined probability, the PAM value corresponding to the currentlytransmitted training data bits.

In some embodiments, the PAM value is a 3-bit PAM8.

It should be appreciated that the operations and features performed bythe optical transmitter 230 described above with reference to FIGS. 2-9are also applicable to the method 1000 and have the same effect, andspecific details thus are not described again.

In some embodiments, a device capable of performing the method 1000 (forexample, the optical transmitter 230 shown in FIG. 2) may includerespective means for performing various steps of the method 1000. Thesemeans can be implemented in any appropriate manner. For example, theymay be implemented by circuits or software modules.

In some embodiments, the apparatus comprises: means for performing aprocess to select a PAM value from a set of candidate PAM values basedon currently transmitted training data bits, transmitted Pulse AmplitudeModulation (PAM) values corresponding to previously transmitted trainingdata bits, and training data bits to be transmitted subsequently, theselected PAM value corresponding to the currently transmitted trainingdata bits; means for transmitting the selected PAM value to an opticalreceiver; means for receiving, from the optical receiver, an indicationas to whether the optical receiver correctly detects the currentlytransmitted training data bits; and means for updating the process atleast in part based on the indication.

In some embodiments, the process is performed by neural network learningor by a table lookup.

In some embodiments, at least one of the following is predefined: thenumber of the transmitted PAM values corresponding to the previouslytransmitted training data bits, the number of the training data bits tobe transmitted subsequently, an order of the PAM, or a value of theindication as to whether the optical receiver correctly detects thecurrently transmitted training data bits.

In some embodiments, means for transmitting the selected PAM value tothe optical receiver comprises: means for performing digital-to-analogconversion on the selected PAM value; and means for transmitting theconverted selected PAM value to the optical receiver.

In some embodiments, means for performing the process comprises: meansfor performing the process to determine a probability that a candidatePAM value is selected from the set of candidate PAM values; and meansfor selecting, based on the determined probability, the PAM valuecorresponding to the currently transmitted training data bits.

In some embodiments, the PAM value is a 3-bit PAM8.

FIG. 11 illustrates a block diagram of a device 1100 adapted toimplement embodiments of the present disclosure. The device 1100 may beimplemented at the optical transmitter 230 or a part of the opticaltransmitter 230 shown in FIG. 2.

As shown in FIG. 11, the device 1100 includes a processor 1110. Theprocessor 1110 controls operations and functionalities of the device1100. For example, in some embodiments, the processor 1100 may performvarious operations by means of instructions 1130 stored in a memory 1120coupled thereto. The memory 1120 may be of any appropriate type adaptedto the local technical environment, and may be implemented using anyappropriate data storage technology, including, but not limited to, asemiconductor-based storage device, a magnetic storage device andsystem, and an optical storage device and system. Although FIG. 11 showsonly one memory unit, there may be a plurality of physically differentmemory units in the device 1100.

The processor 1110 may be of any appropriate type adapted to the localtechnical environment, and may include, but is not limited to, one ormore of a general-purpose computer, a special-purpose computer, amicrocontroller, a Digital Signal Controller (DSP), and acontroller-based multi-core controller model. The device 1100 may alsoinclude a plurality of processors 1110. The device 1100 may include acommunication module (not shown), which may implement informationreceived and transmitted in a wired manner, such as an optical fiber,cable or the like, or a wireless manner.

The processor 1110 performs instructions, causing the device 1100 toperform related operations and features of the optical transmitter 230described above with reference to FIGS. 2-10. All the features describedabove with reference to FIGS. 2-10 are applicable to the device 1100,and details are not described herein again.

In general, various example embodiments of the present disclosure can beimplemented in hardware or special purpose circuits, software, logic, orany combination thereof. Some aspects can be implemented in hardware,while other aspects can be implemented in firmware or software that canbe executed by a controller, microprocessor, or other computing devices.When each aspect of the embodiments of the present disclosure isillustrated or depicted as a block diagram, flowchart, or using someother graphical representations, it would be appreciated that theblocks, devices, systems, techniques, or methods described herein can beimplemented as a non-limiting example in hardware, software, firmware,dedicated circuits or logic, general hardware or controllers or othercomputing devices, or some combinations thereof.

As an example, the embodiments of the present disclosure can bedescribed in the context of machine-executable instructions which areincluded, for example, in a program module executed in a device on atarget physical or virtual processor. Generally speaking, programmodules include routines, programs, libraries, objects, classes,components, data structures and the like, which perform particular tasksor implement particular abstract data structures. In variousembodiments, the functions of the program modules can be combined ordivided between the program modules described herein. Machine-executableinstructions for program modules can be executed locally or within adistributed device. In a distributed device, the program module may belocated in both of a local and a remote storage medium.

Computer program code for implementing methods of the present disclosuremay be written in one or more programming languages. These computerprogram codes may be provided to a processor of a general-purposecomputer, special-purpose computer, or other programmable dataprocessing apparatus, so that the program codes, when executed by thecomputer or other programmable data processing devices, cause thefunctions/operations specified in the flowcharts and/or block diagramsto be implemented. The program code may be executed entirely on acomputer, partly on the computer, as a stand-alone software package,partly on a computer and partly on a remote computer or entirely on aremote computer or server.

In the context of this disclosure, computer program codes or relateddata may be carried by any appropriate carrier to enable a device,apparatus, or a processor to perform various processes and operationsdescribed above. Examples of the carrier include signals, computerreadable media, and the like.

Examples of signals may include electrical, optical, radio, sound, orother forms of propagation, such as carrier waves, infrared signals, andthe like.

A computer readable medium may be any tangible medium that may containor store a program for or in connection with an instruction executionsystem, apparatus, or device. The computer readable medium may be acomputer readable signal medium or a computer readable storage medium. Acomputer readable medium may include, but is not limited to, anelectronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus or device, or any suitable combinationthereof. More detailed examples of computer readable storage mediuminclude an electrical connection with one or more wires, a portablecomputer disk, a hard disk, a random access memory (RAM), a read-onlymemory (ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical storage device, a magnetic storage device, or anysuitable combination thereof.

Further, although operations are depicted in a particular order, thisshould not be construed as requiring that such operations be performedin the particular order shown or in sequential order, or that allillustrated operations be performed to achieve a desirable result. Insome cases, multitasking or parallel processing can be beneficial.Likewise, although the above discussions include certain specificimplementation details, these should not be construed as limiting thescope of any invention or claim, but rather as descriptions of featuresthat may be specific to particular embodiments of a particularinvention. Certain features that are described in this specification inthe context of separate embodiments may also be implemented incombination in a single embodiment. Conversely, various features thatare described in the context of a single embodiment may also beimplemented separately in multiple embodiments or in any suitablesub-combination.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter specified in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

1. An optical transmitter, comprising: at least one processor; anon-transitory memory coupled to the at least one processor and havinginstructions stored therein, the instructions, when executed by the atleast one processor, being configured to perform acts comprising:performing a process to select a pulse amplitude modulation value from aset of candidate pulse amplitude modulation values based on currentlytransmitted training data bits, transmitted pulse amplitude modulationvalues corresponding to previously transmitted training data bits andtraining data bits to be transmitted subsequently, the selected pulseamplitude modulation value corresponding to the currently transmittedtraining data bits; transmitting the selected pulse amplitude modulationvalue to an optical receiver; receiving, from the optical receiver, anindication as to whether the optical receiver correctly detects thecurrently transmitted training data bits; and updating the process atleast in part based on the indication.
 2. The optical transmitter ofclaim 1, wherein the process is performed by neural network learning orby a table lookup.
 3. The optical transmitter of claim 1, wherein atleast one of the following is predefined: the number of the transmittedpulse amplitude modulation values corresponding to the previouslytransmitted training data bits, the number of the training data bits tobe transmitted subsequently, an order of the pulse amplitude modulation,or a value of the indication as to whether the optical receivercorrectly detects the currently transmitted training data bits ispredefined.
 4. The optical transmitter of claim 1, wherein transmittingthe selected pulse amplitude modulation value to the optical receivercomprises: performing digital-to-analog conversion on the selected pulseamplitude modulation value; and transmitting the converted selectedpulse amplitude modulation value to the optical receiver.
 5. The opticaltransmitter of claim 1, wherein performing the process comprises:performing the process to determine a probability that a candidate pulseamplitude modulation value is selected from the set of candidate pulseamplitude modulation values; and selecting, based on the determinedprobability, the pulse amplitude modulation value corresponding to thecurrently transmitted training data bits.
 6. The optical transmitter ofclaim 1, wherein the pulse amplitude modulation value is a 3-bit PAM8.7. A method performed at an optical transmitter, comprising: performinga process to select a pulse amplitude modulation value from a set ofcandidate pulse amplitude modulation values based on currentlytransmitted training data bits, transmitted pulse amplitude modulationvalues corresponding to previously transmitted training data bits, andtraining data bits to be transmitted subsequently, the selected pulseamplitude modulation value corresponding to the currently transmittedtraining data bits; transmitting the selected pulse amplitude modulationvalue to an optical receiver; receiving, from the optical receiver, anindication as to whether the optical receiver correctly detects thecurrently transmitted training data bits; and updating the process atleast in part based on the indication.
 8. The method of claim 7, whereinthe process is performed by neural network learning or by a tablelookup.
 9. The method of claim 7, wherein at least one of the followingis predefined: the number of the transmitted pulse amplitude modulationvalues corresponding to the previously transmitted training data bits,the number of the training data bits to be transmitted subsequently, anorder of the pulse amplitude modulation, or a value of the indication asto whether the optical receiver correctly detects the currentlytransmitted training data bits.
 10. The method of claim 7, whereintransmitting the selected pulse amplitude modulation value to theoptical receiver comprises: performing digital-to-analog conversion onthe selected pulse amplitude modulation value; and transmitting theconverted selected pulse amplitude modulation value after the conversionto the optical receiver. 11.-12. (canceled)
 13. An optical transmitter,comprising: means for performing a process to select a pulse amplitudemodulation value from a set of candidate pulse amplitude modulationvalues based on currently transmitted training data bits, transmittedpulse amplitude modulation values corresponding to previouslytransmitted training data bits, and training data bits to be transmittedsubsequently, the selected pulse amplitude modulation valuecorresponding to the currently transmitted training data bits; means fortransmitting the selected pulse amplitude modulation value to an opticalreceiver; means for receiving, from the optical receiver, an indicationas to whether the optical receiver correctly detects the currentlytransmitted training data bits; and means for updating the process atleast in part based on the indication.
 14. A computer: readable storagemedium having computer program instructions stored thereon, theinstructions, when executed by a processor on an optical transmitter,causing the optical transmitter to perform acts comprising: performing aprocess to select a pulse amplitude modulation value from a set ofcandidate pulse amplitude modulation values based on currentlytransmitted training data bits, transmitted pulse amplitude modulationvalues corresponding to previously transmitted training data bits, andtraining data bits to be transmitted subsequently, the selected pulseamplitude modulation value corresponding to the currently transmittedtraining data bits; transmitting the selected pulse amplitude modulationvalue to an optical receiver; receiving, from the optical receiver, anindication as to whether the optical receiver correctly detects thecurrently transmitted training data bits; and updating the process atleast in part based on the indication.
 15. The computer readable storagemedium of claim 14, wherein the process is performed by neural networklearning or by a table lookup.
 16. The computer readable storage mediumof claim 14, wherein at least one of the following is predefined: thenumber of the transmitted pulse amplitude modulation valuescorresponding to the previously transmitted training data bits, thenumber of the training data bits to be transmitted subsequently, anorder of the pulse amplitude modulation, or a value of the indication asto whether the optical receiver correctly detects the currentlytransmitted training data bits.
 17. The computer readable storage mediumof claim 14, wherein transmitting the selected pulse amplitudemodulation value to the optical receiver comprises: performingdigital-to-analog conversion on the selected pulse amplitude modulationvalue; and transmitting the converted selected pulse amplitudemodulation value after the conversion to the optical receiver. 18.-19.(canceled)